learn_bcDBN

learn_bcDBN is a Java implementation of a learning algorithm for consistent k-graph dynamic Bayesian networks (cDBN) and BFS-consistent k-graph dynamic Bayesian networks (bcDBN) . The program receives a data set with multivariate longitudinal categorical observations and outputs the optimal structure. The algorithm has polynomial time complexity in the number of attributes and observations.

If you use learn_bcDBN in your research please cite:

José L Monteiro, Susana Vinga, and Alexandra M Carvalho. Polynomial-time algorithm for learning optimal tree-augmented dynamic Bayesian networks. In UAI, pages 622–631, 2015.

Usage

In tDBN the input file format is described.

By executing the following .jar file:

$ java -jar learn_bcDBN.jar

The command line options are:

 -c,--compact                 Outputs network in compact format, omitting
                              intra-slice edges. Only works if specified
                              together with -d and with --markovLag 1.
                              
 -d,--dotFormat               Outputs network in dot format, allowing
                              direct redirection into Graphviz to
                              visualize the graph.
                              
 -i,--inputFile <file>        Input CSV file to be used for network
                              learning.
                              
 -m,--markovLag <int>         Maximum Markov lag to be considered, which
                              is the longest distance between connected
                              time-slices. Default is 1, allowing edges
                              from one preceding slice.
                              
 -ns,--nonStationary          Learns a non-stationary network (one
                              transition network per time transition). By
                              default, a stationary DBN is learnt.
                              
 -o,--outputFile <file>       Writes output to <file>. If not supplied,
                              output is written to terminal.
                              
 -p,--numParents <int>        Maximum number of parents from preceding
                              time-slice(s).
                              
 -pm,--parameters             Learns and outputs the network parameters.
 
 -r,--root <int>              Root node of the intra-slice tree. By
                              default, root is arbitrary.
                              
 -s,--scoringFunction <arg>   Scoring function to be used, either MDL or
                              LL. MDL is used by default.  
                              
 -cDBN,---cDBN               Learns cDBN structures. The default is tDBN structures.
 
 
 -bcDBN,---bcDBN             Learns bcDBN structures. The default is tDBN structures.
 
                              
 -ind,---intra_ind            In-degree of the intra-slice transition 
                              network.
                                                       

Example

Consider the data set cDBN1_5000.csv. The following command:

java -jar learn_bcDBN.jar -i cDBN1_5000.csv -p 1 -o res_cDBN -s MDL -cDBN -ind 2 -d

Produces the follwing structure:

And the following command:

java -jar learn_bcDBN.jar -i cDBN1_5000.csv -p 1 -o res_bcDBN -s MDL -bcDBN -ind 2 -d

Produces the follwing structure: